Data Visualization

• Many talented programmers
• Some scan the web and answer issues

https://stackoverflow.com/

Hadley Wickham Yihui Xie

https://yihui.name/en/2017/08/so-gh-email/

https://rmarkdown.rstudio.com/

Many libraries and functions for graphs in R…

• ggplot2 is one of the most elegant and most versatile.

• ggplot implements the grammar of graphics to describe and build graphs.

• Do more and do it faster by learning one system and applying it in many places.

• Learn more about ggplot2 in “The Layered Grammar of Graphics”

http://vita.had.co.nz/papers/layered-grammar.pdf

Example from your data

library(ggplot2)
ggplot(data = participants_data, aes(x=letters_in_first_name, y=days_to_email_response)) + 
  geom_point()

Scatterplot of days to email response as a function of the letters in your first name

Want to understand how all the pieces fit together? See the R for Data Science book: http://r4ds.had.co.nz/

ggplot(data = participants_data, aes(x=letters_in_first_name, y=days_to_email_response, color=academic_parents, size=working_hours_per_day)) + 
  geom_point()

Scatterplot of letters in your first name as a function of days to email response with colors representing binary data related to academic parents and working hours per day as bubble sizes.

Make more graphs

Example from Anderson's iris data set

ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length, color=Species, size=Petal.Width))+ 
  geom_point()

Scatterplot of iris petal length as a function of sepal length with colors representing iris species and petal width as bubble sizes.

ggplot accepts formula arguments such as log

ggplot(data = diamonds, aes(x=carat, y=price, alpha = 0.2)) + geom_point()

ggplot(data = diamonds, aes(x=log(carat), y= log(price), alpha = 0.2)) + geom_point()

library(dplyr)
dsmall <- top_n(diamonds, n=100)
#Plot with different colors for color
ggplot(data = dsmall, aes(x=carat, y=price, color = color))+ geom_point()

#Plot with different shapes for cut 
ggplot( data = dsmall, aes(carat, price, shape = cut)) + geom_point()

Set parameters manually with I() Inhibit Interpretation / Conversion of Objects

ggplot(data = diamonds, aes(carat, price, alpha=I(0.1), color=I("blue"))) + geom_point()

ggplot(data = diamonds, aes(carat, price, alpha=I(0.4), color=I("green"))) + geom_point()

With “geom” different types of plots can be defined e.g. points, line, boxplot, path, smooth. These can also be combined.

ggplot(data=dsmall, aes(x=carat, y=price))+
geom_point()+
geom_smooth()

geom_smooth() selects a smoothing method based on the data. Use method = to specify your preferred smoothing method.

ggplot(data=dsmall, aes(x=carat, y=price))+ geom_point()+ geom_smooth()

ggplot(data=diamonds, aes(x=carat, y=price))+ geom_point()+ 
geom_smooth(method = 'glm')

ggplot2 lines and smoothing options

• Boxplots can be displayed through geom_boxplot().

ggplot(data=diamonds, aes(x=color, y=price/carat)) + 
geom_boxplot()

• Jittered plots geom_jitter() show all points.

ggplot(data=diamonds, aes(x=color, y=price/carat)) + 
geom_boxplot()+ 
geom_jitter()

In case of overplotting changing alpha can help.

ggplot(data=diamonds, aes(x=color, y=price/carat, alpha=I(0.1))) + 
geom_boxplot()+ 
geom_jitter()

ggplot(data = diamonds, aes(x=carat)) +
geom_density()

ggplot(data = diamonds, aes(x=carat, color = color)) +
geom_density()

ggplot(data = diamonds, aes(x=carat, color = color, alpha=I(0.3))) +
geom_density()

ggplot2 histograms

Use factor to subset your data.

ggplot(data = mpg, aes(x=displ, y=hwy,  color = cyl))+ 
geom_point()+
geom_smooth(method="lm")

ggplot(data = mpg, aes(x=displ, y=hwy,  color = factor(cyl)))+ 
geom_point()+
geom_smooth(method="lm")

ggplot2 subset with smooth line

for aes() in ggplot()

• using fewer functions; example - using labs() to add a title instead of ggtitle()
• using functions multiple times; example aes(x = var1) + aes(y = var2) rather than aes(x = var1, y = var2)
• using base R functions and tidyverse functions. For other packages, the :: style to call them
• write out arguments (no shortcuts) aes(x = gdppercap) not aes(gdppercap)

https://evamaerey.github.io/ggplot_flipbook/ggplot_flipbook_xaringan.html#1

ggplot code in non-slow fashion

ggplot(mtcars, aes(mpg, y = hp, col = gear)) +
  geom_point() +
  ggtitle("My Title") +
  labs(x = "the x label", y = "the y label", col = "legend title")

'Slow ggplotting' version for the same plot

  ggplot(data = mtcars) +
  aes(x = mpg) +
  labs(x = "the x label") +
  aes(y = hp) +
  labs(y = "the y label") +
  geom_point() +
  aes(col = gear) +
  labs(col = "legend title") +
  labs(title = "My Title")

https://evamaerey.github.io/ggplot_flipbook/ggplot_flipbook_xaringan.html#1

• Use dplyr, ggplot2 and reshape2

part_data<-select_if(participants_data, is.numeric)

cormat <- round(cor(part_data), 1)
melted_cormat <- melt(cormat)

ggplot(data = melted_cormat, aes(x=Var1, 
y=Var2, fill=value)) + 
geom_tile()

png(file = "cortile.png", width = 7, height = 6, units = "in", res = 300)

ggplot(data = melted_cormat, aes(x = Var1, y = Var2, fill = value)) + geom_tile() + theme(axis.text.x = element_text(angle = 45, hjust = 1))

dev.off()

• Check with journal about size, resolution etc.

?pdf

• Use datasauRus, ggplot2 and gganimate

library(gganimate)
library(datasauRus)

ggplot(datasaurus_dozen, aes(x=x, y=y))+
  geom_point()+
  theme_minimal() +
  transition_states(dataset, 3, 1) + 
  ease_aes('cubic-in-out')

• Use tidyverse, ggplot2 and gganimate

ggplot(data = dsmall, aes(x = carat, y = price, color = color)) + 
  geom_line() +
  transition_reveal(carat) + 
  ease_aes("linear") +
  labs(title='Diamond carat: {frame_along}')

Test your new skills

• Use scatter plots, barcharts and boxplots
• Vary the sample and run the same analysis and plots
• Save your most interesting figure and share it with us tomorrow

Your turn to perform

• Import data from an external source (e.g. FAO, World Bank)
• Display those data in an interactive plot
• Play around with the design
• Export your most interesting figure and share it with us